Unlexicalized Dependency Parser for Variable Word Order Languages Based on Local Contextual Pattern
نویسندگان
چکیده
We investigate the effect of unlexicalization in a dependency parser for variable word order languages and propose an unlexicalized parser which can utilize some contextual information in order to achieve performance comparable to that of lexicalized parsers. Unlexicalization of an early dependency parser makes performance decrease by 3.6%. However, when we modify the unlexicalized parser into the one which can consider additional contextual information, the parser performs better than some lexicalized dependency parsers, while it requires simpler smoothing processes, less time and space for parsing.
منابع مشابه
Feature Engineering in Persian Dependency Parser
Dependency parser is one of the most important fundamental tools in the natural language processing, which extracts structure of sentences and determines the relations between words based on the dependency grammar. The dependency parser is proper for free order languages, such as Persian. In this paper, data-driven dependency parser has been developed with the help of phrase-structure parser fo...
متن کاملA Two Stage Constraint - Based Dependency Parser for Free Word Order Languages
The paper proposes a broad coverage twostage constraint based dependency parser for free word order languages. For evaluating the parser and to ascertain its coverage we show its performance on Hindi which is a free word order language. We compare our results with that of two data-driven parsers which were trained on a subpart of a Hindi Treebank. The final results are good with a maximum attac...
متن کاملUniversal Dependency Parser: A Single Parser for Many Languages on Arc-Swift
Dependency parsing has been a longstanding task in NLP with a recent boost in performance thanks to neural network models. However, most dependency parsers are monolingual–a single parser is trained per language–and utilize transitionbased systems that are limited to local information. We utilize a novel transitionbased system, arc-swift, proposed in [1] that incorporates both local and global ...
متن کاملClustering Words by Syntactic Similarity improves Dependency Parsing of Predicate-argument Structures
We present an approach for deriving syntactic word clusters from parsed text, grouping words according to their unlexicalized syntactic contexts. We then explore the use of these syntactic clusters in leveraging a large corpus of trees generated by a high-accuracy parser to improve the accuracy of another parser based on a different formalism for representing a different level of sentence struc...
متن کاملDependency-based Analysis for Tagalog Sentences
Interest in dependency parsing increased because of its efficiency to represent languages with flexible word order. Many research have applied dependency-based syntactic analysis to different languages and results vary depending on the nature of the language. Languages with more flexible word order structure tend to have lower performances compared to more fixed word order languages. This work ...
متن کامل